Abstract

Fungal endocarditis is a rare subtype of infective endocarditis that often presents with nonspecific symptoms in patients with complex medical histories, making diagnosis challenging. Patients with a history of ALL may present with congestive heart failure, chemo-induced cardiomyopathy, acute coronary syndrome, cardiac lymphomatous metastasis, or infections. We present the case of a patient with a history of ALL who presented with acute coronary syndrome and imaging concerning for primary cardiac lymphoma, when in fact the patient ended up suffering from culture proven fungal endocarditis.

Abstract

BACKGROUND: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with cystic fibrosis (CF), performing similarly to a pediatric radiologist.METHODS: All frontal/lateral chest radiographs (2058 exams) performed in CF patients at a single institution from January 2008-2018 were retrospectively identified, and ground-truth Brasfield scoring performed by a board-certified pediatric radiologist. 1858 exams (90.3%) were used to train and validate the DCNN model, while 200 exams (9.7%) were reserved for a test set. Five board-certified pediatric radiologists independently scored the test set according to the Brasfield method. DCNN model vs. radiologist performance was compared using Spearman correlation (rho) as well as mean difference (MD), mean absolute difference (MAD), and root mean squared error (RMSE) estimation.RESULTS: For the total Brasfield score, rho for the model-derived results computed pairwise with each radiologist's scores ranged from 0.79-0.83, compared to 0.85-0.90 for radiologist vs. radiologist scores. The MD between model estimates of the total Brasfield score and the average score of radiologists was -0.09. Based on MD, MAD, and RMSE, the model matched or exceeded radiologist performance for all subfeatures except air-trapping and large lesions.CONCLUSIONS: A DCNN model is promising for predicting CF Brasfield scores with accuracy similar to that of a pediatric radiologist.

Abstract

The purpose of this study was to assess in pediatric pulmonary artery (PA) reconstruction candidates the feasibility and added utility of preoperative chest computed tomography angiography (CTA) using dual-energy technique, from which perfused blood volume (PBV)/iodine maps can be generated as a surrogate of pulmonary perfusion. Pediatric PA reconstruction patients were prospectively recruited for a new dose-neutral dual-energy CTA protocol. For each case, the severity of anatomic PA obstruction was graded by two pediatric cardiovascular radiologists in consensus using a modified Qanadli index. PBV maps were qualitatively reviewed and auto-segmented using Siemens syngo.via software. Associations between Qanadli scores and PBV were assessed with Spearman correlation (r) and ROC analysis. Effective radiation doses were estimated from dose-length product and ICRP 103k-factors, using cubic Hermite spline interpolation. 19 patients were recruited with mean (SD) age of 6.0 (5.1), 11 (57.9%) female, 11 (73.7%) anesthetized. Higher QS correlated with lower PBV, both on a whole lung (r=-0.54, p<0.001) and lobar (r=-0.50, p<0.001) basis. The lung with lowest absolute PBV was predictive of the lung with highest Qanadli score, with AUC of 0.70 (95% CI 0.47-0.93). Qualitatively, PBV maps were heterogeneous, corresponding to multifocal PA stenoses, with decreased iodine content in areas of most severe obstruction. In conclusion, dual-energy chest CTA is feasible for pediatric PA reconstruction candidates. PBV maps show deficits in regions of more severe anatomic obstruction and may serve as a novel biomarker in this population.

Abstract

The case of a 68-year-old man with chest pain for 3 days is presented. Coronary angiography demonstrated subtotal occlusion of the mid-left anterior descending artery. A drug-eluting cobalt alloy stent was implanted after balloon dilation. On the 3rd postoperative day, echocardiography showed a ventricular septal rupture (VSR) (7 mm diameter) near the cardiac apex and ventricular aneurysm. On cardiac magnetic resonance imaging (MRI), the VSR was shown to be 11 mm in diameter. The membranous septum was 32 and 27.8 mm along the anteroposterior and superoinferior axes, respectively. The left-to-right shunt was apparent. Four weeks later, interventional therapy was performed to occlude the VSR according to the result of the MRI. The symptoms improved rapidly, and the patient was discharged. At the 4-month follow up visit, cardiac MRI revealed no shunt at the occlusion site, and the edge of the occluder was secured in the adjacent normal cardiac tissues. In conclusion, cardiac MRI could be considered for patients with a newly implanted cobalt alloy stent to provide an accurate assessment of VSR.

Abstract

The US Preventive Services Task Force (USPSTF) recommends 1-time sonographic screening for abdominal aortic aneurysms (AAAs) in male smokers ages 65-75 and other selected individuals in this age group based on risk factors. Patients in this age range are frequent utilizers of lumbar spine MRI, in which the abdominal aorta is typically fully imaged. The purpose of this study was to assess the potential detection rate of AAAs on lumbar spine MRI performed in the USPSTF screening age range with systematic aortic measurement and the frequency with which AAAs are currently reported in practice.All consecutive lumbar spine MRI exams performed without contrast at a single academic tertiary care center over a 1-year period (4/1/2016-3/31/2017) in patients ages 65-75 were retrospectively reviewed. Maximal anteroposterior, and transverse dimensions of the abdominal aorta were measured using axial T2-weighted images, supplemented with sagittal T2-weighted images if assessment was limited by field-of-view or artifact. The detection rate of AAA, defined as dilation of the aorta to a diameter of 3 cm, size of AAAs detected, and frequency with which AAAs were reported, were assessed. Differences in aortic diameters and aneurysm detection rates between genders were compared with the unpaired 2-sample t test.Three hundred and ninety-five lumbar spine MRIs were reviewed, 240 (60.8%) in women and 155 (39.2%) in men, with mean standard deviation (SD) age of 70.2 3.2 years. AAAs were detected in 38/395 (9.6%) cases, most (33/38, 86.8%) of which were <4 cm. Of these, only 4 (10.5%) were reported by the interpreting radiologist; 3/4 (75%) corresponded to aneurysms 4 cm.Lumbar spine MRI performed in the USPSTF AAA screening age range, especially in men, facilitates frequent detection of AAA when the aorta is systematically measured. However, in typical lumbar spine assessment, AAAs are often underreported, particularly for smaller aneurysms.

Abstract

Congenital pulmonary artery (PA) anomalies comprise a rare and heterogeneous spectrum of disease, ranging from abnormal origins to complete atresia. They may present in early infancy or more insidiously in adulthood, often in association with congenital heart disease such as tetralogy of Fallot or other syndromes. In recent years, cross-sectional imaging, including computed tomography (CT) and magnetic resonance imaging (MRI), has become widely utilized for the noninvasive assessment of congenital PA diseases, supplementing echocardiography and at times supplanting invasive angiography. In this article, modern CT and MRI techniques for imaging congenital PA disorders are summarized. The key clinical features, cross-sectional imaging findings, and treatment options for the most commonly encountered entities are then reviewed. Emphasis is placed on the ever-growing role of cross-sectional imaging options in facilitating early and accurate diagnosis and tailored treatment.

Abstract

Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models. Our work demonstrates that both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy than the human experts alone. Our work further demonstrates that when used in combination, the swarm-based technology and deep-learning technology outperformed either method alone. The superior diagnostic accuracy of the combined HITL AI solution compared to radiologists and AI alone has broad implications for the surging clinical AI deployment and implementation strategies in future practice.

Abstract

BACKGROUND: General anesthesia (GA) or sedation has been used to obtain good-quality motion-free breath-hold chest CT scans in young children; however pulmonary atelectasis is a common and problematic accompaniment that can confound diagnostic utility. Dual-source multidetector CT permits ultrafast high-pitch sub-second examinations, minimizing motion artifact and potentially eliminating the need for a breath-hold.OBJECTIVE: The purpose of this study was to evaluate the feasibility of free-breathing ultrafast pediatric chest CT without GA and to compare it with breath-hold and non-breath-hold CT with GA.MATERIALS AND METHODS: Young (3years old) pediatric outpatients scheduled for chest CT under GA were recruited into the study and scanned using one of three protocols: GA with intubation, lung recruitment and breath-hold; GA without breath-hold; and free-breathing CT without anesthesia. In all three protocols an ultrafast high-pitch CT technique was used. We evaluated CT images for overall image quality, presence of atelectasis and motion artifacts.RESULTS: We included 101 scans in the study. However the GA non-breath-hold technique was discontinued after 15 scans, when it became clear that atelectasis was a major issue despite diligent attempts to mitigate it. This technique was therefore not included in statistical evaluation (86 remaining patients). Overall image quality was higher (P=0.001) and motion artifacts were fewer (P

Abstract

BACKGROUND: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation.METHODS AND FINDINGS: Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts.CONCLUSIONS: Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

Abstract

BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.METHODS AND FINDINGS: We developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution.CONCLUSIONS: In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.

Abstract

Aortic injury remains a major contributor to morbidity and mortality from acute thoracic trauma. While such injuries were once nearly uniformly fatal, the advent of cross-sectional imaging in recent years has facilitated rapid diagnosis and triage, greatly improving outcomes. In fact, cross-sectional imaging is now the diagnostic test of choice for traumatic aortic injury (TAI), specifically computed tomography angiography (CTA) in the acute setting and CTA or magnetic resonance angiography (MRA) in follow-up. In this review, we present an up-to-date discussion of acute traumatic thoracic aortic injury with a focus on optimal and emerging CT/MR techniques, imaging findings of TAI, and potential pitfalls.

Abstract

Purpose To evaluate the frequency and implications of perivascular fat stranding on coronary computed tomography (CT) angiograms obtained for suspected acute coronary syndrome (ACS). Materials and Methods This retrospective registry study was approved by the institutional review board. The authors reviewed the medical records and images of 1403 consecutive patients (796 men, 607 women; mean age, 52.8 years) who underwent coronary CT angiography at the emergency department from February 2012 to March 2016. Fat attenuation, length and number of circumferential quadrants of the affected segment, and attenuation values in the unaffected epicardial and subcutaneous fat were measured. "Cases" were defined as patients with perivascular fat stranding. Patients with significant stenosis but without fat stranding were considered control subjects. Baseline imaging characteristics, ACS frequency, and results of subsequent downstream testing were compared between cases and control subjects by using two-sample t, Mann-Whitney U, and Fisher tests. Results Perivascular fat stranding was seen in 11 subjects, nine with atherosclerotic lesions and two with spontaneous coronary artery dissections, with a mean fat stranding length of 19.2 mm and circumferential extent averaging 2.9 quadrants. The mean attenuation of perivascular fat stranding, normal epicardial fat, and normal subcutaneous fat was 17, -93.2, and -109.3 HU, respectively (P < .001). Significant differences (P < .05) between cases and control subjects included lower Agatston score, presence of wall motion abnormality, and initial elevation of serum troponin level. ACS frequency was 45.4% in cases and 3.8% in control subjects (P = .001). Conclusion Recognition of perivascular fat stranding may be a helpful additional predictor of culprit lesion and marker of risk for ACS in patients with significant stenosis or spontaneous coronary artery dissection. RSNA, 2018 Online supplemental material is available for this article.

Abstract

Purpose To identify what information patients and parents or caregivers found useful before an imaging examination, from whom they preferred to receive information, and how those preferences related to patient-specific variables including demographics and prior radiologic examinations. Materials and Methods A 24-item survey was distributed at three pediatric and three adult hospitals between January and May 2015. The 2 or Fisher exact test (categorical variables) and one-way analysis of variance or two-sample t test (continuous variables) were used for comparisons. Multivariate logistic regression was used to determine associations between responses and demographics. Results Of 1742 surveys, 1542 (89%) were returned (381 partial, 1161 completed). Mean respondent age was 46.2 years 16.8 (standard deviation), with respondents more frequently female (1025 of 1506, 68%) and Caucasian (1132 of 1504, 75%). Overall, 78% (1117 of 1438) reported receiving information about their examination most commonly from the ordering provider (824 of 1292, 64%), who was also the most preferred source (1005 of 1388, 72%). Scheduled magnetic resonance (MR) imaging or nuclear medicine examinations (P < .001 vs other examination types) and increasing education (P = .008) were associated with higher rates of receiving information. Half of respondents (757 of 1452, 52%) sought information themselves. The highest importance scores for pre-examination information (Likert scale 4) was most frequently assigned to information on examination preparation and least frequently assigned to whether an alternative radiation-free examination could be used (74% vs 54%; P < .001). Conclusion Delivery of pre-examination information for radiologic examinations is suboptimal, with half of all patients and caregivers seeking information on their own. Ordering providers are the predominant and preferred source of examination-related information, with respondents placing highest importance on information related to examination preparation. RSNA, 2018 Online supplemental material is available for this article.

Abstract

Abdominal aortic aneurysms (AAAs) are a leading cause mortality and morbidity but often go undiagnosed until late stages unless imaging is performed. In 2005, the United States Preventive Services Task Force (USPSTF) for the first time recommended one-time ultrasound screening for elderly male smokers and selective screening in other populations. These guidelines were reaffirmed and updated in 2014; a proposal for potential further revisions is now in early planning stages. In this article, we review the past and current USPSTF AAA screening recommendations and techniques for performing optimal screening. Evidence supporting screening and alternative guidelines are also discussed. In addition, emerging concepts and controversies in AAA screening are highlighted, including conflicting data on screening benefits, screening underutilization, inconsistent follow-up recommendations, and the potential for duplicative testing, alternative screening modalities, and clinically significant incidental findings.

Abstract

A variety of syndromes are associated with thoracoabdominal aortic pathologies. While these diseases are collectively rare, the presence of advanced or unusual aortic disease at a young age should raise suspicion of an underlying syndrome. Similarly, patients with a known syndrome require close monitoring in anticipation of future aortic disease. In this article, the syndromes most commonly encountered in clinical practice are reviewed, including Marfan syndrome (MFS) and other connective tissue disorders, Turner syndrome (TS), autosomal dominant polycystic kidney disease (ADPKD), neurofibromatosis (NF), Williams syndrome (WS), Alagille syndrome (AGS), and DiGeorge syndrome (DGS). The distinct clinical, imaging, and management features of each disorder are discussed. Attention is focused on the unique patterns of aortic disease in each syndrome, emphasizing the role of recent imaging modalities and treatment strategies. Ancillary and distinguishing aspects of the syndromes that aid in diagnosis are also highlighted.

Abstract

The cyanotic congenital heart diseases are a rare and heterogeneous group of disorders, often requiring urgent neonatal management. Although echocardiography is the mainstay for imaging, continued technological advances have expanded the role for computed tomography and magnetic resonance imaging, helping to limit invasive cardiac catheterization. In this article, the authors review the broad spectrum of cyanotic congenital heart disease, focusing on the utility of advanced noninvasive imaging modalities while highlighting key clinical features and management considerations.

Abstract

To assess changes in abdominal aortic aneurysm (AAA) ultrasound screening associated with the release of revised U.S. Preventive Services Task Force (USPSTF) recommendations on June 24,2014.All AAA screening ultrasound examinations performed in the Massachusetts General Hospital radiology department in the 15 months before and after the new guidelines were retrospectively reviewed to assess changes in examination volume and appropriateness, demographics, aneurysm detection rate and size at diagnosis, frequency and type of incidental findings, and radiologist recommendations. Examinations were considered "definitely appropriate" if meeting USPSTF grade "B" evidence and "possibly appropriate" if meeting grade "C" or "I" evidence, based on available guidelines. Means were compared with the t test.A total of 831 examinations were reviewed, 417 (50.2%) performed before and 414 (49.8%) after the new guidelines, with overall mean (SD) subject age 67.9 (6.8) years, 89.2% male. Appropriate examinations increased from 289 of 417 (69.3%) to 313 of 414 (75.6%) after the new guidelines (P= .04), mostly due to definitely appropriate examinations (253/417 [60.7%] versus 286/414 [69.1%], P= .01). Aneurysm detection rates increased from 23 of 417 (5.5%) to 39 of 414 (9.4%), P= .03. Mean (SD) aneurysm size (cm) at diagnosis decreased from 3.8 (0.7) to 3.3 (0.6), P= .01. Examination volume, demographics, and rates of incidentals and recommendations remained similar. Incidentals arose in 15.4% of all examinations, often iliac artery aneurysms or renal masses. Recommendations were made in 5.1%, mostly for cross-sectional imaging.The revised USPSTF guidelines have been associated with increased AAA screening appropriateness and aneurysm detection in our practice, with smaller aneurysm size at diagnosis.

Abstract

Highly publicized accounts of radiation overdose from computed tomography (CT) in both children and adults prompted legislation in California regulating CT dose. The purpose of this study was to determine the impact of the law (codified in Senate Bill [SB] 1237) on California radiologist practice patterns and understanding of CT dose.All radiologist members of the California Radiological Society were surveyed in August-September 2013. Questions gauged radiologists' familiarity with and attitudes toward the law, awareness of CT dose, and changes in practice following the law's enactment.Of 1,300 surveyed, 138 (11%) responded; 132 of 137 (96%) were familiar with SB 1237. Of 135 responding, 126 and 115 (93% and 85%, respectively) knew to report CT dose index volume and dose-length product. Sixty of 134 (45%) attributed dose reporting to an increased awareness of appropriate dose ranges. Twenty-nine of 133 (22%) had modified protocols in concert with SB 1237s enactment. Of 31 responding, 5 (16%), 23 (74%), and 3 (74%) had modified protocols in only children, both adults and children, and only adults, respectively. Twenty-four of 129 (19%) utilized automated dose reporting; 48 (37%) and 57 (44%) used dictation/transcription and template-assisted voice recognition, respectively. Forty of 134 (30%) noted delays finalizing CT reports.Most radiologists who responded in our sample were familiar with SB 1237. Nearly half attributed dose reporting to an increased awareness of appropriate dose ranges. Almost one quarter indicated protocol modifications, the majority including children, occurring in conjunction with the law. Reporting inefficiency was a common concern.

Abstract

To evaluate the safety and feasibility of off-label use of ferumoxytol as an intravenous MRI contrast agents in pediatric patients and young adults.With HIPAA compliance and IRB approval, 86 consecutive patients who had undergone 3 T or 1.5 T MRI with ferumoxytol were retrospectively identified. The blood pressure and heart rate of patients before and after ferumoxytol injection were compared. The overall image quality was evaluated independently by two radiologists with a four-point scale. Interobserver agreement was calculated using weighted kappa statistics.The meanstandard deviation (SD) pre and post-contrast systolic blood pressures (SBP) were 10118 and 9520, respectively. There was a statistically significant difference between pre-SBP and post-SBP (P=0.003). The pre-contrast diastolic blood pressure (DBP) and the post-contrast diastolic blood pressure (DBP) were 6014 and 5117, respectively. There was a statistically significant difference between pre-DBP and post-DBP (P<0.001). The number of patients with SBP and DBP increase, SBP increase and DBP decrease, SBP decrease and DBP increase, SBP and DBP decrease, SBP increase and DBP unchanged were 14 (16%), 9 (10%), 6 (7%), 56 (65%), 1 (1%), respectively. There was moderate agreement on all individual assessments of image quality (kappa=0.45). Eighty-two of 86 (95.4%) studies were scored 3 or above (at least diagnostic quality) by both readers, with 90% confidence interval of 92-99%.Ferumoxytol is effective as an MR contrast agent. In our sample, there was on average a small but clinically insignificant drop in SBP and DBP post-contrast injection. Large, randomized, controlled trials are needed to establish optimal dosing, imaging procedures, and safety monitoring.

Abstract

To evaluate the safety and feasibility of off-label use of ferumoxytol as an intravenous MRI contrast agents in pediatric patients and young adults.With HIPAA compliance and IRB approval, 86 consecutive patients who had undergone 3 T or 1.5 T MRI with ferumoxytol were retrospectively identified. The blood pressure and heart rate of patients before and after ferumoxytol injection were compared. The overall image quality was evaluated independently by two radiologists with a four-point scale. Interobserver agreement was calculated using weighted kappa statistics.The meanstandard deviation (SD) pre and post-contrast systolic blood pressures (SBP) were 10118 and 9520, respectively. There was a statistically significant difference between pre-SBP and post-SBP (P=0.003). The pre-contrast diastolic blood pressure (DBP) and the post-contrast diastolic blood pressure (DBP) were 6014 and 5117, respectively. There was a statistically significant difference between pre-DBP and post-DBP (P<0.001). The number of patients with SBP and DBP increase, SBP increase and DBP decrease, SBP decrease and DBP increase, SBP and DBP decrease, SBP increase and DBP unchanged were 14 (16%), 9 (10%), 6 (7%), 56 (65%), 1 (1%), respectively. There was moderate agreement on all individual assessments of image quality (kappa=0.45). Eighty-two of 86 (95.4%) studies were scored 3 or above (at least diagnostic quality) by both readers, with 90% confidence interval of 92-99%.Ferumoxytol is effective as an MR contrast agent. In our sample, there was on average a small but clinically insignificant drop in SBP and DBP post-contrast injection. Large, randomized, controlled trials are needed to establish optimal dosing, imaging procedures, and safety monitoring.

Abstract

Chest masses present a common problem in the perinatal period. Advances in prenatal ultrasound, supplemented by fetal magnetic resonance imaging, now allow early detection and detailed characterization of many thoracic lesions in utero. As such, in asymptomatic infants, assessment with postnatal computed tomography or magnetic resonance imaging can often be delayed for several months until the time at which surgery is being contemplated. Bronchopulmonary malformations comprise most of the thoracic masses encountered in clinical practice. However, a variety of other pathologies can mimic their appearances or produce similar effects such as hypoplasia of a lung or both lungs. Understanding of the key differentiating clinical and imaging features can assist in optimizing prognostication and timely management.

Abstract

The purpose of this study was to determine whether radiologist-parent (guardian) consultation sessions for pediatric ultrasound with immediate disclosure of examination results if desired increases visit satisfaction, decreases anxiety, and increases understanding of the radiologist's role.Parents chaperoning any outpatient pediatric ultrasound were eligible and completed surveys before and after ultrasound examinations. Before the second survey, parents met with a pediatric radiologist on a randomized basis but could opt out and request or decline the consultation. Differences in anxiety and understanding of the radiologist's role before and after the examination were compared, and overall visit satisfaction measures were tabulated.Seventy-seven subjects participated, 71 (92%) of whom spoke to a radiologist, mostly on request. In the consultation group, the mean score (1, lowest; 4, highest) for overall experience was 3.8 0.4 (SD), consultation benefit was 3.7 0.6, and radiologist interaction was 3.7 0.6. Demographics were not predictive of satisfaction with statistical significance in a multivariate model. Forty-six of 68 (68%) respondents correctly described the radiologist's role before consultation. The number increased to 60 (88%) after consultation, and the difference was statistically significant (p < 0.001). There was also a statistically significant decrease in mean anxiety score from 2.0 1.0 to 1.5 0.8 after consultation (p < 0.001). Sixty-four of 70 (91%) respondents indicated that they would prefer to speak with a radiologist during every visit.Radiologist consultation is well received among parents and associated with decreased anxiety and increased understanding of the radiologist's role. The results of this study support the value of routine radiologist-parent interaction for pediatric ultrasound.

Abstract

Effective July 1, 2012, CT dose reporting became mandatory in California. We sought to assess radiologist compliance with this legislation and to determine areas for improvement.We retrospectively reviewed reports from all chest CT examinations performed at our institution from July 1, 2012, through June 30, 2013, for errors in documentation of volume CT dose index (CTDIvol), dose-length product (DLP), and phantom size. Reports were considered as legally compliant if both CTDIvol and DLP were documented accurately and as institutionally compliant if phantom size was also documented accurately. Additionally, we tracked reports that did not document dose in our standard format (phantom size, CTDIvol for each series, and total DLP).Radiologists omitted CTDIvol, DLP, or both in nine of 664 examinations (1.4%) and inaccurately reported one or both of them in 56 of the remaining 655 examinations (8.5%). Radiologists omitted phantom size in 11 of 664 examinations (1.7%) and inaccurately documented it in 20 of the remaining 653 examinations (3.1%). Of 664 examinations, 599 (90.2%) met legal reporting requirements, and 583 (87.8%) met institutional requirements. In reporting dose, radiologists variably used less decimal precision than available, summed CTDIvol, included only series-level DLP, and specified dose information from the scout topogram or a nonchest series for combination examinations.Our institutional processes, which primarily rely on correct human performance, do not ensure accurate dose reporting and are prone to variation in dose reporting format. In view of this finding, we are exploring higher-reliability processes, including better-defined standards and automated dose reporting systems, to improve compliance.